Evidence map›Paper›PMID 39161969›Full record

ArticlePeerJ2024

A retrospective evaluation of individual thigh muscle volume disparities based on hip fracture types in followed-up patients: an AI-based segmentation approach using UNETR.

Hyeon Su Kim, Shinjune Kim, Hyunbin Kim, Sang-Youn Song, Yonghan Cha, Jung-Taek Kim, Jin-Woo Kim, Yong-Chan Ha, Jun-Il Yoo

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Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Hyeon Su KimDepartment of Biomedical Research Institute, Inha University Hospital, Incheon, South Korea.ORCID 0000-0002-6883-5448
Shinjune KimDepartment of Biomedical Research Institute, Inha University Hospital, Incheon, South Korea.
Hyunbin KimDepartment of Biomedical Research Institute, Inha University Hospital, Incheon, South Korea.
Sang-Youn SongDepartment of Orthopaedic Surgery, Gyeongsang National University Hospital, Jinju, South Korea.ORCID 0000-0001-8484-7572
Yonghan ChaDepartment of Orthopaedic Surgery, Daejeon Eulji Medical Center, Daejeon, South Korea.
Jung-Taek KimDepartment of Orthopaedic Surgery, Ajou University School of Medicine, Ajou Medical Center, Suwon, Republic of South Korea.
Jin-Woo KimDepartment of Orthopaedic Surgery, Nowon Eulji Medical Center, Seoul, South Korea.
Yong-Chan HaDepartment of Orthopaedic Surgery, Seoul Bumin Hospital, Seoul, Republic of South Korea.
Jun-Il YooDepartment of Orthopaedic Surgery, Inha University Hospital, Inha University College of Medicine, Incheon, Republic of South Korea.

Funding

Health Industry Development Institute(KHIDI)Korea Health Technology R&D Project through the KoreaMinistry of Health & Welfare, Republic of Korea
6 · The paper itself

Abstract

Background: Hip fractures are a common and debilitating condition, particularly among older adults. Loss of muscle mass and strength is a common consequence of hip fractures, which further contribute to functional decline and increased disability. Assessing changes in individual thigh muscles volume in follow-up patients can provide valuable insights into the quantitative recovery process and guide rehabilitation interventions. However, accurately measuring anatomical individual thigh muscle volume can be challenging due to various, labor intensive and time-consuming. Materials and Methods: This study aimed to evaluate differences in thigh muscle volume in followed-up hip fracture patients computed tomography (CT) scans using an AI based automatic muscle segmentation model. The study included a total of 18 patients at Gyeongsang National University, who had undergone surgical treatment for a hip fracture. We utilized the automatic segmentation algorithm which we have already developed using UNETR (U-net Transformer) architecture, performance dice score = 0.84, relative absolute volume difference 0.019 ± 0.017%. Results: The results revealed intertrochanteric fractures result in more significant muscle volume loss (females: -97.4 cm Conclusions: The use of an automatic muscle segmentation model based on deep learning algorithms enables efficient and accurate analysis of thigh muscle volume differences in followed up hip fracture patients. Our findings emphasize the significant muscle loss tied to sarcopenia, a critical condition among the elderly. Intertrochanteric fractures resulted in greater muscle volume deformities, especially in key muscle groups, across both genders. Notably, while most muscles exhibited volume reduction following hip fractures, the sartorius, vastus and gluteus groups demonstrated more significant disparities in individuals who sustained intertrochanteric fractures. This non-invasive approach provides valuable insights into the extent of muscle atrophy following hip fracture and can inform targeted rehabilitation interventions.

Indexed as

Hip FracturesMuscle, SkeletalThighTomography, X-Ray ComputedAgedAged, 80 and overAlgorithmsArtificial IntelligenceFemaleFollow-Up StudiesHumansMaleOrgan SizeRetrospective StudiesAICTDisparityHip fractureMuscle volumeRadiologySegmentationThighUNETR

Identifiers

PMID39161969
PMCPMC11332390

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LicenceCC BY-NC
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.